CAREER: Physics Regularized Machine Learning Theory: Modeling Stochastic Traffic Flow Patterns for Smart Mobility Systems

职业：物理正则化机器学习理论：为智能移动系统建模随机交通流模式

• 批准号: 2234289
• 负责人: Xianfeng Yang
• 金额: $ 54.41万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234289&HistoricalAwards=false
• 关键词: CAREER; Physics; Regularized; Machine; Learning

This Faculty Early Career Development (CAREER) grant will support fundamental research in modeling stochastic traffic flows for smart mobility systems, based on the fusion of classical transportation models and learning techniques. With the goals of mitigating traffic congestions, improving transportation safety, and reducing vehicle emissions, many smart mobility applications require accurate, reliable, and timely traffic information as input. To meet such needs, this project will lay the foundation of machine learning and traffic flow theory to yield better estimations and predictions of mobility patterns. The method uses transportation domain knowledge to regularize the training process of machine learning. The results will significantly enhance the effectiveness and robustness of those smart mobility applications at both small and large scales. The research activities can be closely integrated with a set of education and outreach activities that include (i) developing a virtual computing lab to facilitate student educations, researcher engagement, government employee training, and industry collaboration, (ii) modernizing the transportation curriculum with research outcomes, (iii) broadening the participation of k-12 students in the annual summer “Transportation Camps” and underrepresented students in the Artificial Intelligence club of a minority-serving institution. Those activities will help transportation students better recognize the importance of engineering knowledge in the era of smart mobility system.The goal of this project is to contribute fundamental theories and a set of markedly improved algorithms to traffic flow modeling. Leveraging the concept of physics regularized machine learning, the research could encode both continuous and discretized traffic flow models into Gaussian process for training regularization. This new model can efficiently resolve the common data sparsity and noise issues and facilitate various smart mobility applications. To accommodate streaming data, this project will also develop a novel physics regularized streaming learning framework that can efficiently improve the model performances in real-time. When dealing with big data, this project can further synergize data of different resolutions, fidelities, and sources to enable sparse Gaussian process and Bayesian committee machine for fast learning. This foundational research can enormously promote machine learning applications in smart mobility systems and contribute to formulating sustainable, scalable, and robust traffic flow models. This project will bridge the gap between classical transportation methods and data-driven approaches.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项教师早期的职业发展（职业）赠款将根据经典运输模型和学习技术的融合，为智能移动系统的随机交通流量进行建模，以支持基础研究。有了减轻交通拥堵，提高运输安全和减少车辆排放的目标，许多智能移动性应用需要准确，可靠且及时的交通信息作为输入。为了满足这种需求，该项目将奠定机器学习和交通流量理论的基础，以获得更好的估计和对流动性模式的预测。该方法使用运输领域知识来规范机器学习的训练过程。结果将显着提高小规模和大型智能移动性应用的有效性和鲁棒性。研究活动可以与一系列教育和外展活动进行紧密整合，包括（i）开发虚拟计算实验室，以支持学生教育，研究人员参与，政府员工培训和行业合作，（ii）通过研究成果进行运输货币，（iii）将K-12学生的参与范围扩大，使K-12学生在年度夏季“交通营业营”中宽广的学生和人工学会宣布一家人的精神，并在人工学会上，一家人的宣传。这些活动将帮助学生更好地意识到在智能移动系统时代工程知识的重要性。该项目的目的是为基本理论和一系列明显改进的算法贡献到交通流建模。为了利用物理正规化机器学习的概念，该研究可以将连续和离散的交通流模型编码为高斯的训练过程。这个新模型可以有效地解决常见的数据稀疏性和噪声问题，并促进各种智能移动性应用程序。为了适应流数据，该项目还将开发出一种新型的物理正规流学习框架，该框架可以有效地实时改善模型性能。在处理大数据时，该项目可以进一步综合不同的分辨率，保真度和来源的数据，以实现稀疏的高斯流程和贝叶斯委员会机器进行快速学习。这项基础研究可以增强智能移动性系统中的机器学习应用，并有助于制定可持续，可扩展和强大的交通流模型。该项目将弥合经典运输方法与数据驱动方法之间的差距。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准来诚实地通过评估来诚实地支持。

项目成果

Discrete macroscopic traffic flow model considering lane-changing behaviors in the mixed traffic environment

混合交通环境下考虑换道行为的离散宏观交通流模型

• 发表时间: 2024
• 期刊: 103rd Transportation Research Board Annual Meeting
• 作者: Yi Zhang, Kaitai Yang

Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah

• DOI: 10.1080/15472450.2022.2157212
• 发表时间: 2022-12-14
• 期刊: JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
• 影响因子: 3.6
• 作者: Gong, Yaobang;Isom, Tanner;Wang, Aaron
• 通讯作者: Wang, Aaron

Traffic Flow Modeling With Gradual Physics Regularized Learning

• DOI: 10.1109/tits.2021.3131333
• 发表时间: 2022-09
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Yun Yuan;Qinzheng Wang;X. Yang

An equitable signalized arterial origin-destination flow estimation by a fairness-aware artificial intelligence

通过具有公平意识的人工智能进行公平的信号化动脉起点-目的地流量估计

• 发表时间: 2024
• 期刊: 103rd Transportation Research Board Annual Meeting
• 作者: Yaobang Gong, Qinzheng Wang

A Transfer Learning–Based LSTM for Traffic Flow Prediction with Missing Data

• DOI: 10.1061/jtepbs.teeng-7638
• 发表时间: 2023-10
• 期刊: Journal of Transportation Engineering, Part A: Systems
• 作者: Zhao Zhang;Hao Yang;Xianfeng Yang